{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `scale_x_discrete`\n",
    "`scale_x_discrete` scale a discrete x-axis. Its parameters are:\n",
    "\n",
    "- `name` - axis label\n",
    "- `breaks` - x tick breaks\n",
    "- `labels` - x tick labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YsWOaNWuWbDabfD6fJkyYoMbGRgWDQYVCIfn9fknS7NmzdezYseh95syZI0maOXOmTpw4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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f503cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (284492717)>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(diamonds, aes(x='cut')) + \\\n",
    "    geom_bar() + \\\n",
    "    scale_x_discrete(\"Cut (Quality)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
